To learn the function \(g_j\) , we use support vector machine (SVM) as a base learner. It is a supervised learning method which can be used for classification and regression. For a regression analysis, it is also known as support vector regression (SVR). To train a model based on SVR, we use “\(\epsilon \) -insensitive” error function which is a symmetric loss function. For this function, a flexible tube with the radius of \(\epsilon \)  is formed symmetrically around the estimate. Errors of size less than \(\epsilon \)  are ignored, and overestimation and underestimation are penalized equally. In other words, some penalty is assigned to the points outside the tube based on the distance between the points and the tube, but no penalty is applied to the points within the tube [1]}.
